diff --git a/aio.html b/aio.html index f8325a9..9aa787d 100644 --- a/aio.html +++ b/aio.html @@ -368,7 +368,7 @@

Content from Introduction to Bioconductor and the SingleCellExperiment class


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Last updated on 2024-10-04 | +

Last updated on 2024-10-07 | Edit this page

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Let’s look at an example dataset. WTChimeraData comes -from a study on mouse development. We can assign one sample to a -SingleCellExperiment object named sce like -so:

+from a study on mouse development Pijuan-Sala et +al.. The study profiles the effect of a transcription factor TAL1 +and its influence on mouse development. Because mutations in this gene +can cause severe developmental issues, Tal1-/- cells (positive for +tdTomato, a fluorescent protein) were injected into wild-type +blastocysts (tdTomato-), forming chimeric embryos.

+

We can assign one sample to a SingleCellExperiment +object named sce like so:

R

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Key Points

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References +

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  1. Pijuan-Sala B, Griffiths JA, Guibentif C et al. (2019). A +single-cell molecular map of mouse gastrulation and early organogenesis. +Nature 566, 7745:490-495.
  2. +
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Content from Exploratory data analysis and quality control

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Content from Exploratory data analysis and quality control


Last updated on 2024-10-03 | @@ -2744,7 +2756,7 @@

Challenge

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We see in the help documentation for ?clusterCells that all of the clustering algorithm details are handled through the @@ -2941,7 +2953,7 @@

Challenge

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You can see that at least among the top markers, cluster 6 (pale green) tends to have the least separation from cluster 1.

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Challenge

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R @@ -3503,7 +3515,7 @@

Challenge

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R @@ -3653,7 +3665,7 @@

Exercise 1: Clustering

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The NNGraphParam constructor has an argument cluster.args. This allows to specify arguments passed on to @@ -3670,7 +3682,7 @@

Give me a hint

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R @@ -3709,7 +3721,7 @@

Exercise 2: Reference marker genes

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R @@ -3782,7 +3794,7 @@

Extension Challenge 1: Group pair comparisons

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One important reason why is because averages over all other clusters can be sensitive to the cell type composition. If a rare cell type shows @@ -3816,7 +3828,7 @@

Extension Challenge 2: Parallelizing SingleR

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Use BiocParallel and the BPPARAM argument! This example will set it to use four cores on your laptop, but you can @@ -3858,7 +3870,7 @@

Extension Challenge 3: Critical inspection of diagnost -
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The example that jumps out most strongly to the eye is ExE endoderm, which doesn’t show clear separate modes. Simultaneously, Endothelium @@ -4151,7 +4163,7 @@

Challenge

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Samples 5 and 6 were from the same “pool” of cells. Looking at the documentation for the dataset under ?WTChimeraData we see @@ -4216,7 +4228,7 @@

Challenge

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False. Batch-level data can be retained through confounding with experimental factors or poor ability to distinguish experimental effects @@ -4669,7 +4681,7 @@

Challenge

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“logFC” stands for log fold-change. edgeR uses a log2 convention. Rather than reporting e.g. a 5-fold increase, it’s better to @@ -4995,7 +5007,7 @@

Exercise 1: Heatmaps

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You can simply hand pheatmap() a matrix as its only argument. pheatmap() has a million options you can tweak, @@ -5009,7 +5021,7 @@

Give me a hint

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R @@ -5044,7 +5056,7 @@

Exercise 2: Model specification and comparison

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After running the second pseudobulk DGE, you can join the two DataFrames of Erythroid3 statistics using the @@ -5059,7 +5071,7 @@

Give me a hint

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R @@ -5142,7 +5154,7 @@

Extension challenge 1: Group effects

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It’s important to have multiple samples within each experimental group because it helps the batch effect correction algorithm distinguish @@ -5219,7 +5231,7 @@

Key Points

-->

Content from Working with large data


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Last updated on 2024-10-04 | +

Last updated on 2024-10-07 | Edit this page

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Challenge

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From ?MulticoreParam :

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ROUTPUT

     approx
-exact   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15
-   1   90   0   0   0   1   0   0   0   1   0   0   0   0   0   0
-   2    0 143   0   0   0   0   0   0   0   0   0   0   0   0   1
-   3    0   0  75   0   2   0   0   0   0   0   0   0   0   0   0
-   4    0   0   0 341   0   0   0   0   0   0   0   0   0   0  56
-   5    0   0   0   0 392   0   0   1   0   1   0   0   0   0   0
-   6    0   0   0   0   0  79 131   0   0   0   0   0   0   0   0
-   7    0   0   0   0   0 245   0   0   0   1   0   0   0   0   0
-   8    0   0   0   0   0   0   0  95   0   0   0   0   0   0   0
-   9    1   0   0   0   2   0   0   0 106   0   0   0   0   0   0
-   10   0   0   0   0   0   0   0   0   0 105   0   0   0   0   0
-   11   0   0   0   0   0   0   1   0   0   5 147   0   0   0   0
-   12   0   0   0   0   1   0   0   0   0  23   0 199   0   0   0
-   13   0   0   0   0   0   0   0   0   0   0   0   0 146   0   0
-   14   0   0   0   0   0   0   0   0   0   0   0   0   0  20   0
+exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 + 1 88 0 0 0 0 0 0 2 0 0 0 0 0 0 + 2 0 86 0 0 0 0 0 0 0 0 0 0 0 0 + 3 0 57 75 1 0 0 0 0 0 0 0 1 0 0 + 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 + 5 0 0 0 0 176 0 0 0 0 0 0 11 0 0 + 6 0 0 0 0 0 73 128 0 0 1 0 0 0 0 + 7 0 0 0 0 0 253 0 0 1 0 0 0 0 0 + 8 1 0 0 0 0 0 0 106 0 0 0 1 0 0 + 9 0 0 0 0 0 0 0 0 113 0 10 0 0 0 + 10 0 0 0 0 0 0 0 0 0 153 0 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 198 0 0 0 + 12 0 0 0 0 0 0 0 0 1 0 0 312 0 0 + 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 56 0 0 0 0 0 0 0 0 0 0

The similarity of the two clusterings can be quantified by calculating the pairwise Rand index:

@@ -5869,7 +5882,7 @@

Challenge

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This code block calculates the exact PCA coordinates. Another thing to note: PC vectors are only identified up to a sign flip. We can see @@ -6247,7 +6260,7 @@

OUTPUT